OpenRaiser/Intern-Atlas huggingface dataset:
Learning Network Dismantling Without Handcrafted Inputs
Venue: arXiv cs.LG 2025
Year: 2025

The application of message-passing Graph Neural Networks has been a breakthrough for important network science problems. However, the competitive performance often relies on using handcrafted structural features as inputs, which increases computational cost and introduces bias into the otherwise purely data-driven network representations. Here, we eliminate the need for handcrafted features by introducing an attention mechanism and utilizing message-iteration profiles, in addition to an effective algorithmic approach to generate a structurally diverse training set of small synthetic networks. Thereby, we build an expressive message-passing framework and use it to efficiently solve the NP-hard problem of Network Dismantling, virtually equivalent to vital node identification, with significant real-world applications. Trained solely on diversified synthetic networks, our proposed model -- MIND: Message Iteration Network Dismantler -- generalizes to large, unseen real networks with millions of nodes, outperforming state-of-the-art network dismantling methods. Increased efficiency and generalizability of the proposed model can be leveraged beyond dismantling in a range of complex network problems.
Deep Joint Source-Channel Coding for Small Satellite Applications
Venue: arXiv cs.NI 2025
Year: 2025

Small satellites used for Earth observation generate vast amounts of high-dimensional data, but their operation in low Earth orbit creates a significant communication bottleneck due to limited contact times and harsh, varying channel conditions. While deep joint source-channel coding (DJSCC) has emerged as a promising technique, its practical application to the complex satellite environment remains an open question. This paper presents a comprehensive DJSCC framework tailored for satellite communications. We first establish a basic system, DJSCC-SAT, and integrate a realistic, multi-state statistical channel model to guide its training and evaluation. To overcome the impracticality of using separate models for every channel condition, we then introduce an adaptable architecture, ADJSCC-SAT, which leverages attention modules to allow a single neural network to adjust to a wide range of channel states with minimal overhead. Through extensive evaluation on Sentinel-2 multi-spectral data, we demonstrate that our adaptable approach achieves performance comparable to using multiple specialized networks while significantly reducing model storage requirements. Furthermore, the adaptable model shows enhanced robustness to channel estimation errors, outperforming the non-adaptable baseline. The proposed framework is a practical and efficient step toward deploying robust, adaptive DJSCC systems for real-world satellite missions.
Overlapping IPv4, IPv6, and TCP data: exploring errors, test case context and multiple overlaps inside network stacks and NIDSes with PYROLYSE
Venue: arXiv cs.NI 2025
Year: 2025

IP fragmentation and TCP segmentation allow for splitting large data packets into smaller ones, e.g., for transmission across network links of limited capacity. These mechanisms permit complete or partial overlaps with different data on the overlapping portions. IPv4, IPv6, and TCP reassembly policies, i.e., the data chunk preferences that depend on the overlap types, differ across protocol implementations. This leads to vulnerabilities, as NIDSes may interpret the packet differently from the monitored host OSes. Some NIDSes, such as Suricata or Snort, can be configured so that their policies are consistent with the monitored OSes. The first contribution of the paper is PYROLYSE, an audit tool that exhaustively tests and describes the reassembly policies of various IP and TCP implementation types. This tool ensures that implementations reassemble overlapping chunk sequences without errors. The second contribution is the analysis of PYROLYSE artifacts. We first show that the reassembly policies are much more diverse than previously thought. Indeed, by testing all the overlap possibilities for n <= 3 test case chunks and different testing scenarios, we observe from 14 to 20 different behaviors out of 23 tested implementations depending on the protocol. Second, we report eight errors impacting one OS, two NIDSes, and two embedded stacks, which can lead to security issues such as NIDS pattern-matching bypass or DoS attacks. A CVE was assigned to a NIDS error. Finally, we show that implemented IP and TCP policies obtained through chunk pair testing are usually inconsistent with the observed triplet reassemblies. Therefore, contrarily to what they currently do, NIDSes or other network traffic analysis tools should not apply n = 2 pair policies when the number of overlapping chunks exceeds two.
Evaluating Angle and Amplitude Encoding Strategies for Variational Quantum Machine Learning: their impact on model's accuracy
Venue: arXiv cs.LG 2025
Year: 2025

Recent advancements in Quantum Computing and Machine Learning have increased attention to Quantum Machine Learning (QML), which aims to develop machine learning models by exploiting the quantum computing paradigm. One of the widely used models in this area is the Variational Quantum Circuit (VQC), a hybrid model where the quantum circuit handles data inference while classical optimization adjusts the parameters of the circuit. The quantum circuit consists of an encoding layer, which loads data into the circuit, and a template circuit, known as the ansatz, responsible for processing the data. This work involves performing an analysis by considering both Amplitude- and Angle-encoding models, and examining how the type of rotational gate applied affects the classification performance of the model. This comparison is carried out by training the different models on two datasets, Wine and Diabetes, and evaluating their performance. The study demonstrates that, under identical model topologies, the difference in accuracy between the best and worst models ranges from 10% to 30%, with differences reaching up to 41%. Moreover, the results highlight how the choice of rotational gates used in encoding can significantly impact the model's classification performance. The findings confirm that the embedding represents a hyperparameter for VQC models.
Explainable AI and Machine Learning for Exam-based Student Evaluation: Causal and Predictive Analysis of Socio-academic and Economic Factors
Venue: arXiv cs.LG 2025
Year: 2025

Academic performance depends on a multivariable nexus of socio-academic and financial factors. This study investigates these influences to develop effective strategies for optimizing students' CGPA. To achieve this, we reviewed various literature to identify key influencing factors and constructed an initial hypothetical causal graph based on the findings. Additionally, an online survey was conducted, where 1,050 students participated, providing comprehensive data for analysis. Rigorous data preprocessing techniques, including cleaning and visualization, ensured data quality before analysis. Causal analysis validated the relationships among variables, offering deeper insights into their direct and indirect effects on CGPA. Regression models were implemented for CGPA prediction, while classification models categorized students based on performance levels. Ridge Regression demonstrated strong predictive accuracy, achieving a Mean Absolute Error of 0.12 and a Mean Squared Error of 0.023. Random Forest outperformed in classification, attaining an F1-score near perfection and an accuracy of 98.68%. Explainable AI techniques such as SHAP, LIME, and Interpret enhanced model interpretability, highlighting critical factors such as study hours, scholarships, parental education, and prior academic performance. The study culminated in the development of a web-based application that provides students with personalized insights, allowing them to predict academic performance, identify areas for improvement, and make informed decisions to enhance their outcomes.
Beyond Fixed: Training-Free Variable-Length Denoising for Diffusion Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Diffusion Large Language Models (DLLMs) are emerging as a powerful alternative to the dominant Autoregressive Large Language Models, offering efficient parallel generation and capable global context modeling. However, the practical application of DLLMs is hindered by a critical architectural constraint: the need for a statically predefined generation length. This static length allocation leads to a problematic trade-off: insufficient lengths cripple performance on complex tasks, while excessive lengths incur significant computational overhead and sometimes result in performance degradation. While the inference framework is rigid, we observe that the model itself possesses internal signals that correlate with the optimal response length for a given task. To bridge this gap, we leverage these latent signals and introduce DAEDAL, a novel training-free denoising strategy that enables Dynamic Adaptive Length Expansion for Diffusion Large Language Models. DAEDAL operates in two phases: 1) Before the denoising process, DAEDAL starts from a short initial length and iteratively expands it to a coarse task-appropriate length, guided by a sequence completion metric. 2) During the denoising process, DAEDAL dynamically intervenes by pinpointing and expanding insufficient generation regions through mask token insertion, ensuring the final output is fully developed. Extensive experiments on DLLMs demonstrate that DAEDAL achieves performance comparable, and in some cases superior, to meticulously tuned fixed-length baselines, while simultaneously enhancing computational efficiency by achieving a higher effective token ratio. By resolving the static length constraint, DAEDAL unlocks new potential for DLLMs, bridging a critical gap with their Autoregressive counterparts and paving the way for more efficient and capable generation.
Legal Knowledge Graph Foundations, Part I: URI-Addressable Abstract Works (LRMoo F1 to schema.org)
Venue: arXiv cs.DL 2025
Year: 2025

Building upon a formal, event-centric model for the diachronic evolution of legal norms grounded in the IFLA Library Reference Model (LRMoo), this paper addresses the essential first step of publishing this model's foundational entity-the abstract legal Work (F1)-on the Semantic Web. We propose a detailed, property-by-property mapping of the LRMoo F1 Work to the widely adopted schema.org/Legislation vocabulary. Using Brazilian federal legislation from the Normas.leg.br portal as a practical case study, we demonstrate how to create interoperable, machine-readable descriptions via JSON-LD, focusing on stable URN identifiers, core metadata, and norm relationships. This structured mapping establishes a stable, URI-addressable anchor for each legal norm, creating a verifiable "ground truth". It provides the essential, interoperable foundation upon which subsequent layers of the model, such as temporal versions (Expressions) and internal components, can be built. By bridging formal ontology with web-native standards, this work paves the way for building deterministic and reliable Legal Knowledge Graphs (LKGs), overcoming the limitations of purely probabilistic models.
Open Science, Open Innovation? The Role of Open Access in Patenting Activity
Venue: arXiv cs.DL 2025
Year: 2025

Scientific knowledge is a key driver of technological innovation, shaping industrial development and policy decisions worldwide. Understanding how patents incorporate scientific research is essential for assessing the role of academic discoveries in technological progress. Non-Patent References (NPRs) provide a useful indicator of this relationship by revealing the extent to which patents draw upon scientific literature. Here, we show that reliance on scientific research in patents varies significantly across regions. Oceania and Europe display stronger engagement with scientific knowledge, while the Americas exhibit lower reliance. Moreover, NPRs are more likely to be open access than the average scientific publication, a trend that intensifies when Sci-Hub availability is considered. These results highlight the transformative impact of Open Science on global innovation dynamics. By facilitating broader access to research, Open Science strengthens the link between academia and industry, underscoring the need for policies that promote equitable and science-based innovation, particularly in developing regions.
PCS Workflow for Veridical Data Science in the Age of AI
Venue: arXiv cs.LG 2025
Year: 2025

Data science is a pillar of artificial intelligence (AI), which is transforming nearly every domain of human activity, from the social and physical sciences to engineering and medicine. While data-driven findings in AI offer unprecedented power to extract insights and guide decision-making, many are difficult or impossible to replicate. A key reason for this challenge is the uncertainty introduced by the many choices made throughout the data science life cycle (DSLC). Traditional statistical frameworks often fail to account for this uncertainty. The Predictability-Computability-Stability (PCS) framework for veridical (truthful) data science offers a principled approach to addressing this challenge throughout the DSLC. This paper presents an updated and streamlined PCS workflow, tailored for practitioners and enhanced with guided use of generative AI. We include a running example to display the PCS framework in action, and conduct a related case study which showcases the uncertainty in downstream predictions caused by judgment calls in the data cleaning stage.
Algorithmic Evaluation and the Marginalization of Single Authorship in Management Science
Venue: arXiv cs.DL 2025
Year: 2025

The decline of single authorship in peer-reviewed journals within the current collaboration-oriented knowledge production framework has prompted deeper reflection on the shifting power structures in academic systems. This paper aims to explore the underlying institutional logic and evaluation mechanisms contributing to the marginalization of single-author research in the management field. It further investigates how the discourse of collaborative advantage conceals structural power redistribution and ideological disembedding. Through an analysis of authorship data from top-tier journals, a critical reading of institutional incentive texts, and an empirical review of authorial configurations, the study building on the work of Harzing, Wuchty, and Lariviere constructs a three-dimensional causal chain: collaboration incentives, responsibility dilution, and originality weakening. Findings suggest that single authorship is not explicitly excluded but is gradually sidelined from central publication channels by funding policies, review practices, and performance metrics. Independent thought is thus structurally marginalized within institutionalized collaboration. The paper advocates for a paradigm shift from instrumental rationality to value-based rationality and calls for the restoration of legitimacy and public value for independent research through reforms in evaluation frameworks, journal governance, and research ethics, aiming to safeguard academic diversity and intellectual autonomy.
Generative AI for CAD Automation: Leveraging Large Language Models for 3D Modelling
Venue: arXiv cs.HC 2025
Year: 2025

Large Language Models (LLMs) are revolutionizing industries by enhancing efficiency, scalability, and innovation. This paper investigates the potential of LLMs in automating Computer-Aided Design (CAD) workflows, by integrating FreeCAD with LLM as CAD design tool. Traditional CAD processes are often complex and require specialized sketching skills, posing challenges for rapid prototyping and generative design. We propose a framework where LLMs generate initial CAD scripts from natural language descriptions, which are then executed and refined iteratively based on error feedback. Through a series of experiments with increasing complexity, we assess the effectiveness of this approach. Our findings reveal that LLMs perform well for simple to moderately complex designs but struggle with highly constrained models, necessitating multiple refinements. The study highlights the need for improved memory retrieval, adaptive prompt engineering, and hybrid AI techniques to enhance script robustness. Future directions include integrating cloud-based execution and exploring advanced LLM capabilities to further streamline CAD automation. This work underscores the transformative potential of LLMs in design workflows while identifying critical areas for future development.
UrbanScore: A Real-Time Personalised Liveability Analytics Platform
Venue: arXiv cs.CY 2025
Year: 2025

This paper introduces UrbanScore - a real-time web platform that computes a personalised liveability score for any urban address. The system fuses five data streams: (i) address geocoding via Nominatim, (ii) facility extraction from OpenStreetMap through Overpass QL, (iii) segment-level traffic metrics from TomTom Flow v10, (iv) hourly air-quality readings from OpenWeatherMap, and (v) user-declared preference profiles, all persisted in an Oracle 19c relational store. Six sub-scores (air, traffic, lifestyle, education, metro access, surface transport) are derived, adaptively weighted and combined; an OpenAI large-language model then converts the numeric results into concise, user-friendly explanations. A pilot deployment covering the 226 km2 metropolitan area of Bucharest evaluated 3,450 unique addresses over four weeks. Median end-to-end latency was 2.1 s (p95 = 2.9s), meeting the <3 non-functional requirement. Aggregate scores ranged from 34 to 92 (mean 68, SD 11), with high-scoring clusters along metro corridors that pair abundant green space with PM2.5 levels below 35 ug m-3. A detailed case study of the Tineretului district produced an overall score of 91/100 and demonstrated how the narrative layer guides users toward comparable neighbourhoods. Limitations include dependence on third-party API uptime, spatial bias toward well-mapped OSM regions and the absence of noise and crime layers, cited by 18% of survey participants as a desired enhancement. Overall, the results show that open geodata, commercial mobility feeds and conversational AI can be integrated into a performant, explainable decision-support tool that places "liveability analytics" in the hands of every house-hunter, commuter and city planner.
Discrete approach to machine learning
Venue: arXiv cs.LG 2025
Year: 2025

The article explores an encoding and structural information processing approach using sparse bit vectors and fixed-length linear vectors. The following are presented: a discrete method of speculative stochastic dimensionality reduction of multidimensional code and linear spaces with linear asymptotic complexity; a geometric method for obtaining discrete embeddings of an organised code space that reflect the internal structure of a given modality. The structure and properties of a code space are investigated using three modalities as examples: morphology of Russian and English languages, and immunohistochemical markers. Parallels are drawn between the resulting map of the code space layout and so-called pinwheels appearing on the mammalian neocortex. A cautious assumption is made about similarities between neocortex organisation and processes happening in our models.
GNN-ASE: Graph-Based Anomaly Detection and Severity Estimation in Three-Phase Induction Machines
Venue: arXiv cs.LG 2025
Year: 2025

The diagnosis of induction machines has traditionally relied on model-based methods that require the development of complex dynamic models, making them difficult to implement and computationally expensive. To overcome these limitations, this paper proposes a model-free approach using Graph Neural Networks (GNNs) for fault diagnosis in induction machines. The focus is on detecting multiple fault types -- including eccentricity, bearing defects, and broken rotor bars -- under varying severity levels and load conditions. Unlike traditional approaches, raw current and vibration signals are used as direct inputs, eliminating the need for signal preprocessing or manual feature extraction. The proposed GNN-ASE model automatically learns and extracts relevant features from raw inputs, leveraging the graph structure to capture complex relationships between signal types and fault patterns. It is evaluated for both individual fault detection and multi-class classification of combined fault conditions. Experimental results demonstrate the effectiveness of the proposed model, achieving 92.5\% accuracy for eccentricity defects, 91.2\% for bearing faults, and 93.1\% for broken rotor bar detection. These findings highlight the model's robustness and generalization capability across different operational scenarios. The proposed GNN-based framework offers a lightweight yet powerful solution that simplifies implementation while maintaining high diagnostic performance. It stands as a promising alternative to conventional model-based diagnostic techniques for real-world induction machine monitoring and predictive maintenance.
Hallucination Detection and Mitigation with Diffusion in Multi-Variate Time-Series Foundation Models
Venue: arXiv cs.LG 2025
Year: 2025

Foundation models for natural language processing have many coherent definitions of hallucination and methods for its detection and mitigation. However, analogous definitions and methods do not exist for multi-variate time-series (MVTS) foundation models. We propose new definitions for MVTS hallucination, along with new detection and mitigation methods using a diffusion model to estimate hallucination levels. We derive relational datasets from popular time-series datasets to benchmark these relational hallucination levels. Using these definitions and models, we find that open-source pre-trained MVTS imputation foundation models relationally hallucinate on average up to 59.5% as much as a weak baseline. The proposed mitigation method reduces this by up to 47.7% for these models. The definition and methods may improve adoption and safe usage of MVTS foundation models.
How Much Is Too Much? Adaptive, Context-Aware Risk Detection in Naturalistic Driving
Venue: arXiv cs.LG 2025
Year: 2025

Reliable risk identification based on driver behavior data underpins real-time safety feedback, fleet risk management, and evaluation of driver-assist systems. While naturalistic driving studies have become foundational for providing real-world driver behavior data, the existing frameworks for identifying risk based on such data have two fundamental limitations: (i) they rely on predefined time windows and fixed thresholds to disentangle risky and normal driving behavior, and (ii) they assume behavior is stationary across drivers and time, ignoring heterogeneity and temporal drift. In practice, these limitations can lead to timing errors and miscalibration in alerts, weak generalization to new drivers/routes/conditions, and higher false-alarm and miss rates, undermining driver trust and reducing safety intervention effectiveness. To address this gap, we propose a unified, context-aware framework that adapts labels and models over time and across drivers via rolling windows, joint optimization, dynamic calibration, and model fusion, tailored for time-stamped kinematic data. The framework is tested using two safety indicators, speed-weighted headway and harsh driving events, and three models: Random Forest, XGBoost, and Deep Neural Network (DNN). Speed-weighted headway yielded more stable and context-sensitive classifications than harsh-event counts. XGBoost maintained consistent performance under changing thresholds, whereas DNN achieved higher recall at lower thresholds but with greater variability across trials. The ensemble aggregated signals from multiple models into a single risk decision, balancing responsiveness to risky behavior with control of false alerts. Overall, the framework shows promise for adaptive, context-aware risk detection that can enhance real-time safety feedback and support driver-focused interventions in intelligent transportation systems.